Effective Analysis of Multilayer Perceptron and Sequential Minimal Optimization in Prediction of Dyscalculia among Primary School Children
Abstract: This study basically focuses on the two classification methods, Multilayer perceptron (MLP) and sequential minimal optimization (SMO), for the prediction of Dyscalculia among primary school children. Prediction of any of the categories of learning disability is not an easy task. Same is the case of dyscalculia. Detail knowledge of the subject is mandatory in accurate prediction of dyscalculia in any child. A sooner the detection faster we can overcome it which will help the child for bright future. Among above mentioned classifiers MLP gives us best accuracy results. This study will also reflect on determining the best classification method for our specific domain.
Keywords: Dyscalculia, MLP, SMO, Classification
How to Cite?: Sampada Margaj, Dr. Seema Purohit, "Effective Analysis of Multilayer Perceptron and Sequential Minimal Optimization in Prediction of Dyscalculia among Primary School Children", Volume 5 Issue 8, August 2016, International Journal of Science and Research (IJSR), Pages: 581-585, https://www.ijsr.net/getabstract.php?paperid=ART2016861, DOI: https://dx.doi.org/10.21275/ART2016861